Tackling limited simulation and small signals

2019/07/24 by C. Argüelles, Carlos A. Argüelles, Austin Schneider +5
Decision Sciences · Mathematics · Physics and Astronomy · #Algorithm #Computer science #Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Mathematics #Maximum likelihood #Poisson distribution #Simulation Techniques and Applications #Statistical Methods and Bayesian Inference #Statistics #Statistics and Probability (physics.data-an) #hep-ex #physics.data-an

paper · pdf · doi:10.48550/arxiv.1907.10636

published in arXiv (Cornell University) (Cornell University) · Proceedings of the 36th International Cosmic Ray Conference (ICRC 2019), Madison, WI, U.S.A

arxiv created 2019/07/24 · openalex publication_date 2019/07/24 · arxiv updated 2019/07/26 · openalex created_date 2019/07/30 · openalex updated_date 2026/08/08

Abstract

We present a new, analytic, Poisson likelihood derived, technique to account for the statistical uncertainties inherent in simulation samples of limited size. This method has better coverage properties than other techniques, is valid for small data samples, and maintains good computational performance.

Related

• Likelihood description for comparing data with simulation of limited statistics
• Coverage Of Confidence Intervals For Poisson Statistics In Presence Of Systematic Uncertainties
• Optimization-based Quantification of Simulation Input Uncertainty via Empirical Likelihood
• The shortest confidence interval for Poisson mean
• Exact Computation of Minimum Sample size for Estimation of Poisson Parameters